Robot Path Planning Using Improved Ant Colony Algorithm in the Environment of Internet of Things

It is a research topic of practical significance to study the path planning technology mobile robot navigation technology. Aiming at problems slow convergence speed, redundant path, and easy fall into local optimal value ant colony algorithm in complex environment, based on improved proposed. First, grid method used model which marks each make move from initial target for search. Second, divided according different tasks. Let some ants explore way first, carry out basic optimization map environment. The antecedent mark advantage with pheromone concentration so as guide subsequent route-finding operation main colony. Finally, order avoid individual falling deadlock state early search, obstacle avoidance factor increased, transition probability improved, amount information dynamically adjusted information, excessive pheromones. Experimental results show that has high global search ability, significantly speeds up can effectively improve efficiency planning.